{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bc2f6249",
   "metadata": {},
   "source": [
    "# 环境配置"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb24f26d",
   "metadata": {},
   "source": [
    "conda install -c conda-forge opencv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa4a077",
   "metadata": {},
   "source": [
    "# 模块读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0b37e0a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f827e911",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "665ea09f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "630d6502",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cfeb3acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b300d60a",
   "metadata": {},
   "source": [
    "# 数据下载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fc8460ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/zwl/miniconda3/envs/cv/bin/python'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sys.executable # 查看当前的python运行环境"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7125085e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mchar_test_a',\n",
       " 'README.md',\n",
       " 'mchar_val',\n",
       " '.ipynb_checkpoints',\n",
       " 'download.sh',\n",
       " 'mchar_sample_submit_A.csv',\n",
       " 'mchar_train.json',\n",
       " '__MACOSX',\n",
       " 'mchar_val.json',\n",
       " 'mchar_data_list_0515.csv',\n",
       " 'eda.ipynb',\n",
       " 'mchar_train',\n",
       " 'model.point',\n",
       " 'unzip.sh']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eb4968a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_json = json.load(open('mchar_train.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c249a46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'height': [219, 219],\n",
       " 'label': [1, 9],\n",
       " 'left': [246, 323],\n",
       " 'top': [77, 81],\n",
       " 'width': [81, 96]}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_json['000000.png']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bab6ac4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "000000.png\n",
      "{'height': [219, 219], 'label': [1, 9], 'left': [246, 323], 'top': [77, 81], 'width': [81, 96]}\n",
      "000001.png\n",
      "{'height': [32, 32], 'label': [2, 3], 'left': [77, 98], 'top': [29, 25], 'width': [23, 26]}\n",
      "000002.png\n",
      "{'height': [15, 15], 'label': [2, 5], 'left': [17, 25], 'top': [5, 5], 'width': [8, 9]}\n",
      "000003.png\n",
      "{'height': [34, 34], 'label': [9, 3], 'left': [57, 72], 'top': [13, 13], 'width': [15, 13]}\n",
      "000004.png\n",
      "{'height': [46, 46], 'label': [3, 1], 'left': [52, 74], 'top': [7, 10], 'width': [21, 15]}\n"
     ]
    }
   ],
   "source": [
    "count = 5 # 这里只显示打印5张照片\n",
    "for pic, data in train_json.items():\n",
    "    if count == 0:\n",
    "        break\n",
    "    print(pic)\n",
    "    print(data)\n",
    "    count -= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a1b1b014",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_json(d):\n",
    "    res = np.array([\n",
    "        d['top'], d['height'], d['left'], d['width'], d['label']\n",
    "    ])\n",
    "    res = res.astype(int)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7ded3e8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('mchar_train/000000.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "796a35df",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('mchar_train/000000.png')\n",
    "arr = parse_json(train_json['000000.png'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "205ae96a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 77,  81],\n",
       "       [219, 219],\n",
       "       [246, 323],\n",
       "       [ 81,  96],\n",
       "       [  1,   9]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr # 矩阵的列数为图片中的数字数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "34d8e403",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx in range(arr.shape[1]):\n",
    "    plt.subplot(1, arr.shape[1]+1, idx+2)\n",
    "    plt.imshow(img[arr[0, idx]:arr[0, idx]+arr[1, idx],arr[2, idx]:arr[2, idx]+arr[3, idx]])\n",
    "    plt.title(arr[4, idx])\n",
    "    plt.xticks([]); plt.yticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c01ef06",
   "metadata": {},
   "source": [
    "# 赛题思路分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8d25483",
   "metadata": {},
   "source": [
    "- 定长字符数据识别\n",
    "- 先检查下数据集中字符最长的标签有多长"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3215f6bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "train_json = json.load(open('mchar_train.json'))\n",
    "max_len = 0\n",
    "for img_name in os.listdir('mchar_train/'):\n",
    "   max_len = max(max_len, len(train_json[img_name]['label']))\n",
    "print(max_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7b07e9a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "val_json = json.load(open('mchar_val.json'))\n",
    "max_len = 0\n",
    "for img_name in os.listdir('mchar_val/'):\n",
    "   max_len = max(max_len, len(val_json[img_name]['label']))\n",
    "print(max_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c699c7a2",
   "metadata": {},
   "source": [
    "训练集中，图片数字字符最长有6， 测试集中最长数字字符有5，该题思路可以看成定长字符数据识别：\n",
    "\n",
    "也就是本来是1,2.因为最长字符有6.所以看成，1，2，x，x，x，x。每个位置看成一个分类问题。每个位置的类别为11, 0-9 一共10，还有x.这里使用数字10来代替x。表示该位置是空着的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04cbbd02",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bf9a5a42",
   "metadata": {},
   "source": [
    "# 数据读取与扩增"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c5d8872",
   "metadata": {},
   "source": [
    "- 数据扩增有什么用？\n",
    "\n",
    "    可以增大数据集，训练的时候可以有效防止过拟合。\n",
    "\n",
    "- 有哪些数据扩增的方法？\n",
    "\n",
    "    在数据扩增方法中，一般会从图像颜色，尺寸，形态，空间和像素等角度进行变换。可以多种变换方法相互组合。\n",
    "\n",
    "    对于图像分类：数据扩增一般不会改变标签\n",
    "\n",
    "    对于物体检测：数据扩增会改变物体坐标位置\n",
    "\n",
    "    这些扩增都很符合直觉。\n",
    "\n",
    "    使用：torchvision 模块中的 transforms函数进行变换，可以直接调用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a3bbc163",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 图像处理中很重要的几个库\n",
    "from PIL import Image\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a8f6bff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mchar_test_a',\n",
       " 'README.md',\n",
       " 'mchar_val',\n",
       " '.ipynb_checkpoints',\n",
       " 'download.sh',\n",
       " 'mchar_sample_submit_A.csv',\n",
       " 'mchar_train.json',\n",
       " '__MACOSX',\n",
       " 'mchar_val.json',\n",
       " 'mchar_data_list_0515.csv',\n",
       " 'eda.ipynb',\n",
       " 'mchar_train',\n",
       " 'model.point',\n",
       " 'unzip.sh']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e01259f",
   "metadata": {},
   "source": [
    "首先介绍下这几个文件是干什么的：\n",
    "\n",
    "- mchar_val:image path\n",
    "- mchar_val.json:validation image label\n",
    "- mchar_train:train image path\n",
    "- mchar_train.json: train image label\n",
    "\n",
    "首先构造数据集，pytorch中数据集是先用dataset构造，然后用dataloader来装载数据集进行训练。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "23286148",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d68f35c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 首先构建my dataset，这个类对象中一定要有三个魔法方法，init， getitem，len\n",
    "class Mydataset(Dataset):\n",
    "    def __init__(self, img_path, json_path, transform=True):\n",
    "        self.img_path = img_path\n",
    "        self.json_path = json_path\n",
    "        self.transform = transform\n",
    "        if self.transform:\n",
    "            self.transforms = transforms.Compose([\n",
    "              # 缩放到固定尺寸\n",
    "              transforms.Resize((64, 128)),\n",
    "              # 随机颜色变换\n",
    "              transforms.ColorJitter(0.2, 0.2, 0.2),\n",
    "              # 加入随机旋转\n",
    "              transforms.RandomRotation(5),\n",
    "              # 将图片转换为pytorch 的tesntor\n",
    "              transforms.ToTensor(),\n",
    "              # 对图像像素进行归一化\n",
    "              transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])\n",
    "            ])\n",
    "        # get json data\n",
    "        self.img_json = json.load(open(self.json_path))\n",
    "        # get imge name list\n",
    "        self.img_list = sorted(os.listdir(self.img_path))\n",
    "    def __getitem__(self, idx):\n",
    "        img_name = self.img_list[idx]\n",
    "        img = Image.open(self.img_path + '/' + img_name).convert('RGB')\n",
    "        if self.transform:\n",
    "            img = self.transforms(img)\n",
    "        label = self.img_json[img_name]['label']\n",
    "        label += (6-len(label))*[10]\n",
    "        # 将label转换成tensor\n",
    "        label = torch.tensor(np.array(label)).long()\n",
    "        return img, label\n",
    "    def __len__(self):\n",
    "        return len(self.img_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8559f4b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = Mydataset('./mchar_train', './mchar_train.json')\n",
    "val_dataset = Mydataset('./mchar_val', './mchar_val.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7f4de684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 10000)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_dataset), len(val_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "57c90ee2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(train_dataset, shuffle=True, batch_size=10)\n",
    "val_loader = DataLoader(val_dataset, shuffle=True, batch_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "84398646",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([10, 3, 64, 128])\n",
      "torch.Size([10, 6])\n"
     ]
    }
   ],
   "source": [
    "# 检查下构造的数据和标签\n",
    "for feature, label in train_loader:\n",
    "    print(feature.shape)\n",
    "    print(label.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da3c8b66",
   "metadata": {},
   "source": [
    "# CNN模型构建"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc802a05",
   "metadata": {},
   "source": [
    "官方baseline：https://tianchi.aliyun.com/notebook-ai/detail?postId=108342"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cce5b501",
   "metadata": {},
   "source": [
    "## CNN模型的使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2c2abf22",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torch\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "61f69576",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 固定随机数种子\n",
    "def set_seed(seed=42):\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "\n",
    "set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "23b9316c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class cnn(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.cnn = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=(3, 3), stride=(2, 2)),\n",
    "            nn.ReLU(),\n",
    "            nn.AdaptiveAvgPool2d((3, 3)),\n",
    "            nn.Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2)),\n",
    "            nn.ReLU(),\n",
    "            nn.AdaptiveAvgPool2d((3, 3)),\n",
    "        )\n",
    "        self.fc1 = nn.Linear(32*3*3, 11)\n",
    "        self.fc2 = nn.Linear(32*3*3, 11)\n",
    "        self.fc3 = nn.Linear(32*3*3, 11)\n",
    "        self.fc4 = nn.Linear(32*3*3, 11)\n",
    "        self.fc5 = nn.Linear(32*3*3, 11)\n",
    "        self.fc6 = nn.Linear(32*3*3, 11)\n",
    "    def forward(self, img):\n",
    "        x = self.cnn(img)\n",
    "        x = x.reshape(x.shape[0], -1)\n",
    "        x1 = self.fc1(x)\n",
    "        x2 = self.fc2(x)\n",
    "        x3 = self.fc3(x)\n",
    "        x4 = self.fc4(x)\n",
    "        x5 = self.fc5(x)\n",
    "#        x6 = self.fc6(x)\n",
    "        return x1, x2, x3, x4, x5# x6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "b950ef2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = cnn()\n",
    "epochs = 50\n",
    "lr = 0.0001\n",
    "gamma = 0.9\n",
    "step_size=5\n",
    "\n",
    "device_count = torch.cuda.device_count()\n",
    "USE_CUDA = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda:0\" if USE_CUDA else \"cpu\")\n",
    "\n",
    "if device_count > 1:\n",
    "    model = nn.DataParallel(model,device_ids=range(device_count)) # multi-GPU\n",
    "    model.to(device)\n",
    "\n",
    "else:\n",
    "    model = model.cuda()\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=lr)\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma) # 学习方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b33bb24d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 要这个图片中的所有数字都识别对了，才算这个图片识别正确。\n",
    "def get_acc(x1, x2, x3, x4, x5, label):\n",
    "    res1 = x1.argmax(dim=1)\n",
    "    res2 = x2.argmax(dim=1)\n",
    "    res3 = x3.argmax(dim=1)\n",
    "    res4 = x4.argmax(dim=1)\n",
    "    res5 = x5.argmax(dim=1)\n",
    "    res = torch.vstack([res1, res2, res3, res4, res5,]).T\n",
    "    res = res.cpu().numpy()\n",
    "    label = label.cpu().numpy()\n",
    "    preds = [''.join(map(str, x[x!=10])) for x in res]\n",
    "    label = [''.join(map(str, x[x!=10])) for x in label]\n",
    "    return (np.array(preds) == np.array(label)).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "1d6236c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "aa2383ca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3000/3000 [01:05<00:00, 46.02it/s]\n",
      "100%|██████████| 1000/1000 [00:19<00:00, 50.15it/s]\n",
      "  0%|          | 5/3000 [00:00<01:05, 45.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH:0, loss:7.114682674407959, train_acc:0.020166666666666666, val_acc:0.0173\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3000/3000 [01:06<00:00, 45.37it/s]\n",
      "100%|██████████| 1000/1000 [00:20<00:00, 49.22it/s]\n",
      "  0%|          | 5/3000 [00:00<01:13, 40.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH:1, loss:5.27589225769043, train_acc:0.0203, val_acc:0.0167\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 17%|█▋        | 513/3000 [00:11<00:56, 44.01it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-326-6a5e3d42d285>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mval_total_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mfeature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m         \u001b[0mfeature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1177\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1178\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1179\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1180\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    515\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 517\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    518\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    555\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 557\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    558\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-101-73dc30e746f7>\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimg_path\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mimg_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'RGB'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m         \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimg_json\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mimg_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0mlabel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torchvision/transforms/transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    891\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torchvision/transforms/transforms.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m    271\u001b[0m             \u001b[0mPIL\u001b[0m \u001b[0mImage\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mRescaled\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m         \"\"\"\n\u001b[0;32m--> 273\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torchvision/transforms/functional.py\u001b[0m in \u001b[0;36mresize\u001b[0;34m(img, size, interpolation)\u001b[0m\n\u001b[1;32m    373\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    374\u001b[0m         \u001b[0mpil_interpolation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpil_modes_mapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 375\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF_pil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpil_interpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    376\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mF_t\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torchvision/transforms/functional_pil.py\u001b[0m in \u001b[0;36mresize\u001b[0;34m(img, size, interpolation)\u001b[0m\n\u001b[1;32m    226\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    229\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36mresize\u001b[0;34m(self, size, resample, box, reducing_gap)\u001b[0m\n\u001b[1;32m   1933\u001b[0m                 )\n\u001b[1;32m   1934\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1935\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1936\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1937\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# strat training \n",
    "total_loss = []\n",
    "train_acc = []\n",
    "val_acc = []\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    \n",
    "    train_total_acc = 0\n",
    "    val_total_acc = 0\n",
    "    \n",
    "    for feature, label in tqdm(train_loader):\n",
    "        feature = feature.cuda()\n",
    "        label = label.cuda()\n",
    "        x1, x2, x3, x4, x5 = model(feature)\n",
    "        train_total_acc += get_acc(x1, x2, x3, x4, x5, label)\n",
    "        loss = criterion(x1, label[:, 0]) + \\\n",
    "        criterion(x2, label[:, 1]) + \\\n",
    "        criterion(x3, label[:, 2]) + \\\n",
    "        criterion(x4, label[:, 3]) + \\\n",
    "        criterion(x5, label[:, 4]) + \\\n",
    "        criterion(x5, label[:, 5])\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    \n",
    "    train_acc.append(train_total_acc/len(train_dataset))\n",
    "    \n",
    "    \n",
    "    model.eval()        \n",
    "    with torch.no_grad():\n",
    "        for feature, label in tqdm(val_loader):\n",
    "            feature = feature.cuda()\n",
    "            label = label.cuda()\n",
    "            x1, x2, x3, x4, x5 = model(feature)\n",
    "            val_total_acc += get_acc(x1, x2, x3, x4, x5, label)\n",
    "        val_acc.append(val_total_acc/len(val_dataset))\n",
    "        \n",
    "    print(f'EPOCH:{epoch}, loss:{loss}, train_acc:{train_acc[epoch]}, val_acc:{val_acc[epoch]}')  \n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "605ad7aa",
   "metadata": {},
   "source": [
    "## 代码优化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "af5a6d12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 代码优化，可以将训练和验证写成两个函数，这样美观又直接\n",
    "def model_train(model, train_loader, train_acc, train_dataset, criterion):\n",
    "    model.train()\n",
    "    train_total_acc = 0\n",
    "    for feature, label in tqdm(train_loader):\n",
    "        feature = feature.cuda()\n",
    "        label = label.cuda()\n",
    "        x1, x2, x3, x4, x5 = model(feature)\n",
    "        train_total_acc += get_acc(x1, x2, x3, x4, x5, label)\n",
    "        loss = criterion(x1, label[:, 0]) + \\\n",
    "        criterion(x2, label[:, 1]) + \\\n",
    "        criterion(x3, label[:, 2]) + \\\n",
    "        criterion(x4, label[:, 3]) + \\\n",
    "        criterion(x5, label[:, 4])\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    total_loss.append(loss)\n",
    "    train_acc.append(train_total_acc/len(train_dataset)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "278bf1ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_val(model, val_loader, val_acc, val_dataset):\n",
    "    model.eval()        \n",
    "    val_total_acc = 0\n",
    "    with torch.no_grad():\n",
    "        for feature, label in tqdm(val_loader):\n",
    "            feature = feature.cuda()\n",
    "            label = label.cuda()\n",
    "            x1, x2, x3, x4, x5 = model(feature)\n",
    "            val_total_acc += get_acc(x1, x2, x3, x4, x5, label)\n",
    "        val_acc.append(val_total_acc/len(val_dataset))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cac6a28",
   "metadata": {},
   "source": [
    "## 保存最佳权重并加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "d701d41b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/3000 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[10,  0, 10,  6,  4],\n",
      "        [10,  9, 10,  4,  4],\n",
      "        [10,  3,  9,  4,  4],\n",
      "        [10,  9,  9,  4,  4],\n",
      "        [10,  0,  9,  4,  4],\n",
      "        [ 8,  0, 10,  6,  4],\n",
      "        [10,  3,  9,  4,  4],\n",
      "        [ 8,  0, 10,  6,  4],\n",
      "        [10,  0,  9,  9,  3],\n",
      "        [10,  7, 10,  6,  4]], device='cuda:0')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-293-4111981c81ed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mtrain_best\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mmodel_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mmodel_val\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'EPOCH:{epoch}, loss:{total_loss[epoch]}, train_acc:{train_acc[epoch]}, val_acc:{val_acc[epoch]}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-285-f433af94701f>\u001b[0m in \u001b[0;36mmodel_train\u001b[0;34m(model, train_loader, train_acc, train_dataset, criterion)\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx5\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mtrain_total_acc\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mget_acc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-290-fa8497e3052a>\u001b[0m in \u001b[0;36mget_acc\u001b[0;34m(x1, x2, x3, x4, x5, label)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mres1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;31m#    res = res1 & res2 & res3 & res4 & res5 & res6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m#    res = res1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# strat training \n",
    "total_loss = []\n",
    "train_acc = []\n",
    "val_acc = []\n",
    "\n",
    "train_best = 1000\n",
    "for epoch in range(epochs):\n",
    "    model_train(model, train_loader, train_acc, train_dataset, criterion,)\n",
    "    model_val(model, val_loader, val_acc, val_dataset) \n",
    "    print(f'EPOCH:{epoch}, loss:{total_loss[epoch]}, train_acc:{train_acc[epoch]}, val_acc:{val_acc[epoch]}')  \n",
    "    # 保存最佳权重\n",
    "    if total_loss[epoch] < train_best:\n",
    "        train_best = total_loss[epoch]\n",
    "        torch.save(model.state_dict(), './model.point')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "9ece7e07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取模型权重\n",
    "model.load_state_dict(torch.load('model.point')) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29a7f5af",
   "metadata": {},
   "source": [
    "## 加入dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84c4a009",
   "metadata": {},
   "source": [
    "dropout相当于机器学习中的集成学习,本质是随机丢弃一些神经元，其实相当于每次随机使用一些属性进行训练。这样能有效防止过你和。在测试的时候，神经元不进行丢弃，所有神经元都使用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "684363aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加入dropout后的神经网络\n",
    "class cnn(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.cnn = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=(3, 3), stride=(2, 2)),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.2),\n",
    "            nn.AdaptiveAvgPool2d((3, 3)),\n",
    "            nn.Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2)),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.2),\n",
    "            nn.AdaptiveAvgPool2d((3, 3)),\n",
    "        )\n",
    "        self.fc1 = nn.Linear(32*3*3, 11)\n",
    "        self.fc2 = nn.Linear(32*3*3, 11)\n",
    "        self.fc3 = nn.Linear(32*3*3, 11)\n",
    "        self.fc4 = nn.Linear(32*3*3, 11)\n",
    "        self.fc5 = nn.Linear(32*3*3, 11)\n",
    "        self.fc6 = nn.Linear(32*3*3, 11)\n",
    "    def forward(self, img):\n",
    "        x = self.cnn(img)\n",
    "        x = x.reshape(x.shape[0], -1)\n",
    "        print(x.shape[0])\n",
    "        assert 0\n",
    "        x1 = self.fc1(x)\n",
    "        x2 = self.fc2(x)\n",
    "        x3 = self.fc3(x)\n",
    "        x4 = self.fc4(x)\n",
    "        x5 = self.fc5(x)\n",
    "        x6 = self.fc6(x)\n",
    "        return x1, x2, x3, x4, x5, x6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "627b3c5d",
   "metadata": {},
   "source": [
    "# Resnet模型构建"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31e67be0",
   "metadata": {},
   "source": [
    "这里改进为resnet18, 可以用resnet18前面的卷积来进行特征提取。使用线性层来进行分类。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3004f0",
   "metadata": {},
   "source": [
    "## Resnet18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "203b5419",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.models as models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "5984e7d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Resnet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        # 调用resnet18模型\n",
    "        conv_model = models.resnet18(pretrained=True)\n",
    "        # 修改为自适应\n",
    "        # conv_model.argpool = nn.AdaptiveAvgPool2d(1)\n",
    "        # 不要最后一层\n",
    "        self.cnn = nn.Sequential(*list(conv_model.children())[:-1])\n",
    "        \n",
    "        self.fc1 = nn.Linear(512, 11)\n",
    "        self.fc2 = nn.Linear(512, 11)\n",
    "        self.fc3 = nn.Linear(512, 11)\n",
    "        self.fc4 = nn.Linear(512, 11)\n",
    "        self.fc5 = nn.Linear(512, 11)\n",
    "        \n",
    "    def forward(self, img):\n",
    "        x = self.cnn(img)\n",
    "        x = x.reshape(x.shape[0], -1)\n",
    "        x1 = self.fc1(x)\n",
    "        x2 = self.fc2(x)\n",
    "        x3 = self.fc3(x)\n",
    "        x4 = self.fc4(x)\n",
    "        x5 = self.fc5(x)\n",
    "        return x1, x2, x3, x4, x5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "6858a717",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = Resnet()\n",
    "epochs = 50\n",
    "lr = 0.0001\n",
    "gamma = 0.9\n",
    "step_size=5\n",
    "\n",
    "device_count = torch.cuda.device_count()\n",
    "USE_CUDA = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda:0\" if USE_CUDA else \"cpu\")\n",
    "\n",
    "if device_count > 1:\n",
    "    model = nn.DataParallel(model,device_ids=range(device_count)) # multi-GPU\n",
    "    model.to(device)\n",
    "\n",
    "else:\n",
    "    model = model.cuda()\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=lr)\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma) # 学习方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "842fa8c0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3000/3000 [01:57<00:00, 25.56it/s]\n",
      "100%|██████████| 1000/1000 [00:25<00:00, 38.78it/s]\n",
      "  0%|          | 0/3000 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH:0, loss:2.1231274604797363, train_acc:0.3381, val_acc:0.3757\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3000/3000 [01:58<00:00, 25.33it/s]\n",
      "100%|██████████| 1000/1000 [00:25<00:00, 39.86it/s]\n",
      "  0%|          | 3/3000 [00:00<02:00, 24.86it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH:1, loss:2.208861827850342, train_acc:0.5618666666666666, val_acc:0.4387\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3000/3000 [01:55<00:00, 25.94it/s]\n",
      "100%|██████████| 1000/1000 [00:24<00:00, 40.12it/s]\n",
      "  0%|          | 0/3000 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH:2, loss:1.7274543046951294, train_acc:0.6424333333333333, val_acc:0.4733\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3000/3000 [01:55<00:00, 25.96it/s]\n",
      "100%|██████████| 1000/1000 [00:24<00:00, 40.12it/s]\n",
      "  0%|          | 0/3000 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH:3, loss:1.5108904838562012, train_acc:0.6971333333333334, val_acc:0.4877\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3000/3000 [01:54<00:00, 26.21it/s]\n",
      "100%|██████████| 1000/1000 [00:24<00:00, 40.71it/s]\n",
      "  0%|          | 3/3000 [00:00<01:50, 27.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH:4, loss:3.190574884414673, train_acc:0.7417666666666667, val_acc:0.4865\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 77%|███████▋  | 2310/3000 [01:30<00:26, 26.03it/s]"
     ]
    }
   ],
   "source": [
    "# strat training \n",
    "total_loss = []\n",
    "train_acc = []\n",
    "val_acc = []\n",
    "\n",
    "train_best = 1000\n",
    "for epoch in range(epochs):\n",
    "    model_train(model, train_loader, train_acc, train_dataset, criterion,)\n",
    "    model_val(model, val_loader, val_acc, val_dataset) \n",
    "    print(f'EPOCH:{epoch}, loss:{total_loss[epoch]}, train_acc:{train_acc[epoch]}, val_acc:{val_acc[epoch]}')  \n",
    "    # 保存最佳权重\n",
    "    if total_loss[epoch] < train_best:\n",
    "        train_best = total_loss[epoch]\n",
    "        torch.save(model.state_dict(), './model.point')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d05307",
   "metadata": {},
   "source": [
    "## Resnet50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0be15841",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1aec7211",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Resnet50(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        # 调用resnet50模型\n",
    "        conv_model = models.resnet50(pretrained=True)\n",
    "        # 修改为自适应\n",
    "        for name, layer in conv_model.named_modules():\n",
    "            if isinstance(layer, nn.MaxPool2d):\n",
    "                conv_model.maxpool = nn.AdaptiveAvgPool2d((1, 1)) \n",
    "        # 不要最后一层\n",
    "        self.cnn = nn.Sequential(*list(conv_model.children())[:-1])\n",
    "        \n",
    "        self.dense1 = nn.Linear(2048, 1024)\n",
    "        self.dense2 = nn.Linear(1024, 512)\n",
    "        \n",
    "        self.fc1 = nn.Linear(512, 11)\n",
    "        self.fc2 = nn.Linear(512, 11)\n",
    "        self.fc3 = nn.Linear(512, 11)\n",
    "        self.fc4 = nn.Linear(512, 11)\n",
    "        self.fc5 = nn.Linear(512, 11)\n",
    "        \n",
    "    def forward(self, img):\n",
    "        x = self.cnn(img)\n",
    "        x = x.reshape(x.shape[0], -1)\n",
    "        x = self.dense1(F.relu(x))\n",
    "        x = self.dense2(F.relu(x))\n",
    "        \n",
    "        x1 = self.fc1(x)\n",
    "        x2 = self.fc2(x)\n",
    "        x3 = self.fc3(x)\n",
    "        x4 = self.fc4(x)\n",
    "        x5 = self.fc5(x)\n",
    "        return x1, x2, x3, x4, x5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c0b1d39e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = Resnet50()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4246f539",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 50\n",
    "lr = 0.001\n",
    "gamma = 0.9\n",
    "step_size=5\n",
    "device_count = torch.cuda.device_count()\n",
    "USE_CUDA = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda:0\" if USE_CUDA else \"cpu\")\n",
    "if device_count > 1:\n",
    "    model = nn.DataParallel(model,device_ids=range(device_count)) # multi-GPU\n",
    "    model.to(device)\n",
    "else:\n",
    "    model = model.cuda()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=lr)\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma) # 学习方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e0079532",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3000/3000 [03:34<00:00, 13.99it/s]\n",
      "100%|██████████| 1000/1000 [00:30<00:00, 33.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH:0, loss:5.335813999176025, train_acc:0.019233333333333335, val_acc:0.0155\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3000/3000 [03:22<00:00, 14.85it/s]\n",
      "100%|██████████| 1000/1000 [00:29<00:00, 33.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH:1, loss:5.222123146057129, train_acc:0.0202, val_acc:0.0155\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 79%|███████▉  | 2379/3000 [02:28<00:38, 16.04it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-4111981c81ed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mtrain_best\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mmodel_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mmodel_val\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'EPOCH:{epoch}, loss:{total_loss[epoch]}, train_acc:{train_acc[epoch]}, val_acc:{val_acc[epoch]}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-38-692d25be5408>\u001b[0m in \u001b[0;36mmodel_train\u001b[0;34m(model, train_loader, train_acc, train_dataset, criterion)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mtrain_total_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mfeature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0mfeature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1177\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1178\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1179\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1180\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    515\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 517\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    518\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    555\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 557\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    558\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-25-73dc30e746f7>\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimg_path\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mimg_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'RGB'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m         \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimg_json\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mimg_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0mlabel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torchvision/transforms/transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    891\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torchvision/transforms/transforms.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, tensor)\u001b[0m\n\u001b[1;32m    219\u001b[0m             \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mNormalized\u001b[0m \u001b[0mTensor\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m         \"\"\"\n\u001b[0;32m--> 221\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torchvision/transforms/functional.py\u001b[0m in \u001b[0;36mnormalize\u001b[0;34m(tensor, mean, std, inplace)\u001b[0m\n\u001b[1;32m    326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m     \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 328\u001b[0;31m     \u001b[0mmean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    329\u001b[0m     \u001b[0mstd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mstd\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# strat training \n",
    "total_loss = []\n",
    "train_acc = []\n",
    "val_acc = []\n",
    "\n",
    "train_best = 1000\n",
    "for epoch in range(epochs):\n",
    "    model_train(model, train_loader, train_acc, train_dataset, criterion,)\n",
    "    model_val(model, val_loader, val_acc, val_dataset) \n",
    "    print(f'EPOCH:{epoch}, loss:{total_loss[epoch]}, train_acc:{train_acc[epoch]}, val_acc:{val_acc[epoch]}')  \n",
    "    # 保存最佳权重\n",
    "    if total_loss[epoch] < train_best:\n",
    "        train_best = total_loss[epoch]\n",
    "        torch.save(model.state_dict(), './model.point')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "471972b7",
   "metadata": {},
   "source": [
    "# MobileNets网络构建"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d87166ad",
   "metadata": {},
   "source": [
    "参考：\n",
    "- https://www.jiqizhixin.com/graph/technologies/c0bc9a32-bd3d-4df2-b0de-c433419a19e6\n",
    "- https://zhuanlan.zhihu.com/p/31551004\n",
    "- 代码：https://flashgene.com/archives/122108.html?spm=5176.21852664.0.0.4e097a98SZxo38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "id": "64b5ad6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MobileNets(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv_model = models.mobilenet_v2(pretrained=True)\n",
    "#        self.conv_model.classifier = nn.Sequential(\n",
    "#            nn.AdaptiveAvgPool2d((7, 7))\n",
    "#            )\n",
    "        # 不要最后一层\n",
    "        self.cnn = nn.Sequential(*list(self.conv_model.children())[:-1])\n",
    "        \n",
    "        self.fc1 = nn.Linear(1280, 11)\n",
    "        self.fc2 = nn.Linear(1280, 11)\n",
    "        self.fc3 = nn.Linear(1280, 11)\n",
    "        self.fc4 = nn.Linear(1280, 11)\n",
    "        self.fc5 = nn.Linear(1280, 11)\n",
    "        \n",
    "    def forward(self, img):\n",
    "        x = self.cnn(img)\n",
    "        x = x.reshape(x.shape[0], -1)\n",
    "        print(x.shape[1])\n",
    "        assert 0\n",
    "        x1 = self.fc1(x)\n",
    "        x2 = self.fc2(x)\n",
    "        x3 = self.fc3(x)\n",
    "        x4 = self.fc4(x)\n",
    "        x5 = self.fc5(x)\n",
    "        return x1, x2, x3, x4, x5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "id": "84f588b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[MobileNetV2(\n",
       "   (features): Sequential(\n",
       "     (0): ConvBNActivation(\n",
       "       (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "       (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       (2): ReLU6(inplace=True)\n",
       "     )\n",
       "     (1): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
       "           (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (2): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
       "           (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (3): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
       "           (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (4): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
       "           (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (5): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
       "           (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (6): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
       "           (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (7): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n",
       "           (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (8): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (9): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (10): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (11): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (12): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
       "           (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (13): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
       "           (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (14): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n",
       "           (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (15): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
       "           (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (16): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
       "           (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (17): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
       "           (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (18): ConvBNActivation(\n",
       "       (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "       (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       (2): ReLU6(inplace=True)\n",
       "     )\n",
       "   )\n",
       "   (classifier): Sequential(\n",
       "     (0): AdaptiveAvgPool2d(output_size=(7, 7))\n",
       "   )\n",
       " ),\n",
       " Sequential(\n",
       "   (0): Sequential(\n",
       "     (0): ConvBNActivation(\n",
       "       (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "       (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       (2): ReLU6(inplace=True)\n",
       "     )\n",
       "     (1): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
       "           (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (2): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
       "           (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (3): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
       "           (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (4): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
       "           (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (5): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
       "           (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (6): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
       "           (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (7): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n",
       "           (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (8): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (9): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (10): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (11): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
       "           (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (12): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
       "           (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (13): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
       "           (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (14): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n",
       "           (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (15): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
       "           (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (16): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
       "           (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (17): InvertedResidual(\n",
       "       (conv): Sequential(\n",
       "         (0): ConvBNActivation(\n",
       "           (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "           (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (1): ConvBNActivation(\n",
       "           (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
       "           (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (2): ReLU6(inplace=True)\n",
       "         )\n",
       "         (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "         (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "     )\n",
       "     (18): ConvBNActivation(\n",
       "       (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "       (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       (2): ReLU6(inplace=True)\n",
       "     )\n",
       "   )\n",
       " ),\n",
       " Linear(in_features=1280, out_features=11, bias=True),\n",
       " Linear(in_features=1280, out_features=11, bias=True),\n",
       " Linear(in_features=1280, out_features=11, bias=True),\n",
       " Linear(in_features=1280, out_features=11, bias=True),\n",
       " Linear(in_features=1280, out_features=11, bias=True)]"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in model.children()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "cec1a712",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = MobileNets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "0293a9a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 50\n",
    "lr = 0.001\n",
    "gamma = 0.9\n",
    "step_size=5\n",
    "device_count = torch.cuda.device_count()\n",
    "USE_CUDA = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda:0\" if USE_CUDA else \"cpu\")\n",
    "if device_count > 1:\n",
    "    model = nn.DataParallel(model,device_ids=range(device_count)) # multi-GPU\n",
    "    model.to(device)\n",
    "else:\n",
    "    model = model.cuda()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=lr)\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma) # 学习方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "id": "8dd91875",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/3000 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10240\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-495-4111981c81ed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mtrain_best\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mmodel_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mmodel_val\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'EPOCH:{epoch}, loss:{total_loss[epoch]}, train_acc:{train_acc[epoch]}, val_acc:{val_acc[epoch]}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-327-692d25be5408>\u001b[0m in \u001b[0;36mmodel_train\u001b[0;34m(model, train_loader, train_acc, train_dataset, criterion)\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0mfeature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx5\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0mtrain_total_acc\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mget_acc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/cv/lib/python3.9/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    891\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-491-2949691671a2>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0;32massert\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m         \u001b[0mx1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0mx2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# strat training \n",
    "total_loss = []\n",
    "train_acc = []\n",
    "val_acc = []\n",
    "\n",
    "train_best = 1000\n",
    "for epoch in range(epochs):\n",
    "    model_train(model, train_loader, train_acc, train_dataset, criterion,)\n",
    "    model_val(model, val_loader, val_acc, val_dataset) \n",
    "    print(f'EPOCH:{epoch}, loss:{total_loss[epoch]}, train_acc:{train_acc[epoch]}, val_acc:{val_acc[epoch]}')  \n",
    "    # 保存最佳权重\n",
    "    if total_loss[epoch] < train_best:\n",
    "        train_best = total_loss[epoch]\n",
    "        torch.save(model.state_dict(), './model.point')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b5a0b40",
   "metadata": {},
   "source": [
    "# Yolo 目标检测"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec6eafac",
   "metadata": {},
   "source": [
    "参考：\n",
    "- 理论参考:https://www.cnblogs.com/xiaoboge/p/10544336.html\n",
    "- 代码参考:http://www.babyitellyou.com/details?id=607272d90a6c640f8b4618c8\n",
    "- 代码参考:使用yolo5训练自己的数据集https://blog.csdn.net/weixin_48994268/article/details/115282688"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d28eceb3",
   "metadata": {},
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a073a08",
   "metadata": {},
   "source": [
    "- [ ] 学习tta，测试集训练三次取平均https://tianchi.aliyun.com/notebook-ai/detail?spm=5176.12586969.1002.18.2ce823e6FF4FLX&postId=108656\n",
    "- [ ] 看下官方的baseline：https://tianchi.aliyun.com/notebook-ai/detail?postId=108342"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "124a1732",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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